production

Recently, I wrote a post about how to deploy deep learning models into production without the use of additional frameworks. This approach was simplistic and works, but there is also TFX (tensorflow x), which is meant for production use cases….

After weeks of training and optimizing a neural net at some point it might be ready for production. Most deep learning projects never reach this point and for the rest it’s time to think about frameworks and technology stack. The…

This is the last post in the series about machine learning in practice. This time the post will be about productionizing machine learning models. I want to share my experience from several production machine learning systems and show how it…

Preprocessing and data transformation are the most important parts of all machine learning pipelines. No matter what type of model you use, if the preprocessing pipeline is buggy, your model will deliver wrong predictions . This remains also true, if…

In this How-To series, I want to share my experience with machine learning models in productions environments. This starts with the general differences to typical software projects and how to acquire and deal with data sets in such projects, goes…